LlamaIndex Llms Integration: Openai
Installation
To install the required package, run:
%pip install llama-index-llms-openai
Setup
- Set your OpenAI API key as an environment variable. You can replace
"sk-..."with your actual API key:
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
Basic Usage
Generate Completions
To generate a completion for a prompt, use the complete method:
from llama_index.llms.openai import OpenAI
resp = OpenAI().complete("Paul Graham is ")
print(resp)
Chat Responses
To send a chat message and receive a response, create a list of ChatMessage instances and use the chat method:
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality."
),
ChatMessage(role="user", content="What is your name?"),
]
resp = OpenAI().chat(messages)
print(resp)
Streaming Responses
Stream Complete
To stream responses for a prompt, use the stream_complete method:
from llama_index.llms.openai import OpenAI
llm = OpenAI()
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
Stream Chat
To stream chat responses, use the stream_chat method:
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
llm = OpenAI()
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality."
),
ChatMessage(role="user", content="What is your name?"),
]
resp = llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
Configure Model
You can specify a particular model when creating the OpenAI instance:
llm = OpenAI(model="gpt-3.5-turbo")
resp = llm.complete("Paul Graham is ")
print(resp)
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality."
),
ChatMessage(role="user", content="What is your name?"),
]
resp = llm.chat(messages)
print(resp)
Asynchronous Usage
You can also use asynchronous methods for completion:
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
resp = await llm.acomplete("Paul Graham is ")
print(resp)
Set API Key at a Per-Instance Level
If desired, you can have separate LLM instances use different API keys:
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo", api_key="BAD_KEY")
resp = OpenAI().complete("Paul Graham is ")
print(resp)
LLM Implementation example
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